Make a violin plot in Python using Matplotlib

What does a violin plot signify ?

• The white dot refers to the median.
• The end points of the bold line represent the iqr1 and iqr3.
• The end points of the thin line represent the min and max similar to the box plot.
• The distribution above 1.5x interquartile(min, max end points of the thin line) denotes the presence of outliers.

Syntax: violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, quantiles=None, points=100, bw_method=None, *, data=None) Parameters: dataset: Array or a sequence of vectors. The input data. positions: array-like, default = [1, 2, ..., n]. Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert: bool, default = True. If true, creates a vertical violin plot. Otherwise, creates a horizontal violin plot. widths: array-like, default = 0.5 Either a scalar or a vector that sets the maximal width of each violin. The default is 0.5, which uses about half of the available horizontal space. showmeans: bool, default = False If True, will toggle rendering of the means. showextrema: bool, default = True If True, will toggle rendering of the extrema. showmedians: bool, default = False If True, will toggle rendering of the medians. quantiles: array-like, default = None If not None, set a list of floats in interval [0, 1] for each violin, which stands for the quantiles that will be rendered for that violin. points: scalar, default = 100 Defines the number of points to evaluate each of the gaussian kernel density estimations at. bw_method: str, scalar or callable, optional The method used to calculate the estimator bandwidth. This can be 'scott', 'silverman', a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a GaussianKDE instance as its only parameter and return a scalar. If None (default), 'scott' is used.

import numpy as np
import matplotlib.pyplot as plt

# creating a list of 
# uniformly distributed values
uniform = np.arange(-100, 100)

# creating a list of normally
# distributed values
normal = np.random.normal(size = 100)*30

# creating figure and axes to
# plot the image
fig, (ax1, ax2) = plt.subplots(nrows = 1, 
                               ncols = 2,
                               figsize =(9, 4),
                               sharey = True)

# plotting violin plot for
# uniform distribution
ax1.set_title('Uniform Distribution')
ax1.set_ylabel('Observed values')
ax1.violinplot(uniform)


# plotting violin plot for 
# normal distribution
ax2.set_title('Normal Distribution')
ax2.violinplot(normal)

# Function to show the plot
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from random import randint

# Creating 3 empty lists
l1 = []
l2 =[]
l3 =[]

# Filling the lists with random value
for i in range(100):
    n = randint(1, 100)
    l1.append(n)
    
for i in range(100):
    n = randint(1, 100)
    l2.append(n)
    
for i in range(100):
    n = randint(1, 100)
    l3.append(n)

random_collection = [l1, l2, l3]

# Create a figure instance
fig = plt.figure()

# Create an axes instance
ax = fig.gca()

# Create the violinplot
violinplot = ax.violinplot(random_collection)
plt.show()